# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0(the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http:  // www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == ==
from tests.mark_utils import arg_mark
import pytest
import numpy as np
import mindspore.context as context
import mindspore.nn as nn
import mindspore.ops as ops
from mindspore import Tensor, Parameter
from mindspore import dtype as mstype

param_shape = [2, 3, 2]


class Net(nn.Cell):
    def __init__(self, epsilon, clip_threshold, beta1, beta2, weight_decay, lr):
        super(Net, self).__init__()
        self.epsilon = epsilon
        self.clip_threshold = clip_threshold
        self.beta1 = beta1
        self.beta2 = beta2
        self.weight_decay = weight_decay
        self.lr = lr
        self.opt = ops.FusedAdaFactor()
        self.param = Parameter(Tensor(np.ones(param_shape), mstype.float32), name="param")
        self.exp_avg = Parameter(Tensor(np.zeros(param_shape), mstype.float32), name="exp_avg")
        self.exp_avg_sq = Parameter(Tensor(np.zeros(param_shape), mstype.float32), name="exp_avg_sq")
        self.exp_avg_sq_row = Parameter(Tensor(np.zeros([2, 3]), mstype.float32), name="exp_avg_sq_row")
        self.exp_avg_sq_col = Parameter(Tensor(np.zeros([2, 2]), mstype.float32), name="exp_avg_sq_col")

    def construct(self, grad):
        out = self.opt(self.epsilon, self.clip_threshold, self.beta1, self.beta2, self.weight_decay, self.lr, grad,
                       self.param, self.exp_avg,
                       self.exp_avg_sq_row, self.exp_avg_sq_col, self.exp_avg_sq)
        return out


class NetWithGlobalNorm(Net):
    def __init__(self, epsilon, clip_threshold, beta1, beta2, weight_decay, lr):
        super(NetWithGlobalNorm, self).__init__(epsilon, clip_threshold, beta1, beta2, weight_decay, lr)
        self.opt = ops.FusedAdaFactorWithGlobalNorm()

    def construct(self, grad, global_norm):
        out = self.opt(self.epsilon, self.clip_threshold, self.beta1, self.beta2, self.weight_decay, self.lr, grad,
                       self.param, self.exp_avg,
                       self.exp_avg_sq_row, self.exp_avg_sq_col, self.exp_avg_sq, global_norm)
        return out


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_adafactor():
    '''
    Feature: AdaFactor
    Description: Test AdaFactor
    Expectation: Run success
    '''
    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
    net = Net((1e-30, 1e-3), 1.0, 0.9, 0.8, 1e-2, 0.03)
    gradient = Tensor(np.ones(param_shape), mstype.float32)
    net(gradient)
    diff = net.param.asnumpy() - np.ones(param_shape) * 0.97
    assert np.all(diff < 1e-3)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_adafactor_with_global_norm():
    '''
    Feature: AdaFactor
    Description: Test AdaFactorWithGlobalNorm
    Expectation: Run success
    '''
    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
    net = NetWithGlobalNorm((1e-30, 1e-3), 1.0, 0.9, 0.8, 1e-2, 0.03)
    gradient = Tensor(np.ones(param_shape), mstype.float32)
    net(gradient, 10.0)
    diff = net.param.asnumpy() - np.ones(param_shape) * 0.97
    assert np.all(diff < 1e-3)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='essential')
def test_adafactor_dynamic_shape():
    '''
    Feature: AdaFactor
    Description: Test AdaFactor with dynamic shape
    Expectation: Run success
    '''
    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
    net = Net((1e-30, 1e-3), 1.0, 0.9, 0.8, 1e-2, 0.03)
    gradient = Tensor(np.ones(param_shape), mstype.float32)
    x_dynamic = Tensor(shape=[None for _ in param_shape], dtype=mstype.float32)
    net.set_inputs(x_dynamic)
    net(gradient)
    diff = net.param.asnumpy() - np.ones(param_shape) * 0.97
    assert np.all(diff < 1e-3)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='essential')
def test_adafactor_with_global_norm_dynamic_shape():
    '''
    Feature: AdaFactor
    Description: Test AdaFactorWithGlobalNorm with dynamic shape
    Expectation: Run success
    '''
    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
    net = NetWithGlobalNorm((1e-30, 1e-3), 1.0, 0.9, 0.8, 1e-2, 0.03)
    gradient = Tensor(np.ones(param_shape), mstype.float32)
    x_dynamic = Tensor(shape=[None for _ in param_shape], dtype=mstype.float32)
    net.set_inputs(x_dynamic, 10.0)
    net(gradient, 10.0)
    diff = net.param.asnumpy() - np.ones(param_shape) * 0.97
    assert np.all(diff < 1e-3)
